Net, Blogs and Rock'n'Roll

When do you want to hear 30-second recommendation clips?

I know the question I'm about to ask must have been posed many times before, but I'm not sure I've ever seen an answer. I've been revisiting my thinking about the different 'modes' in which we listen to music. Sometimes we're open to discovering new artists and songs, sometimes we just want to listen to our favourites. Sometimes we want listen to music in 'lean back' mode where we surrender control of track sequence to a DJ, a playlist we made earlier or even a playlisting algorithm; and sometimes we're more prepared to interact and intervene, 'leaning forward' to sequence the music track by track.

Into this spectrum of listening behaviours come recommender systems like MyStrands (above in the image on the right) and iLike (below) that offer me suggestions for branching my listening based on what I'm listening to in iTunes at this moment. But they offer only 30-second clips of each suggestion, and the suggestions disappear at the end of the track I'm listening to. So my question: in what context would listeners break off in the middle of listening to a track they had chosen to hear a clip (usually fairly lo-fi) of something they hadn't chosen — and quite likely hadn't heard of?

Is this user experience just a reflection of licensing constraints that will disappear as soon as the services concerned have licensed full tracks? Or was it designed that way on purpose, irrespective of legal parameters?

When I was talking about criteria for recommender systems a few weeks ago, I suggested that the key pragmatic measure of a good system is the degree to which people keep coming back for more recommendations. So how much do users actually engage with the 30-second recommendations? Does anyone have any figures they could share — or even anecdotal evidence?

And if people do break off their current listening to attend to recommendation clips are they more likely to follow up on those recommendations, or to forget about them?

Going beyond music, radio producers insert trailers for other programmes in their features (especially on the BBC!), but would you ever insert a trailer for a film in the middle of another one? TV does that, but then people pay a premium price to watch films without such interruptions, suggesting that this isn't seen as adding value.

How community builds audiences and makes discovery stick

At a music marketing event in London a couple of days ago, Scott Cohen, Co-founder of The Orchard (and on the left in the collage), presented a nice thought experiment that demonstrates the importance of community and narrative in making sure that discoveries stick in the mind of audiences. What if Simon Cowell (on the right in the collage) just cut to the chase on The X Factor and presented only one show in each series, saying "we've done the research, we've done the auditions, we've consulted audiences, and here's the winner"? The result would be the same — a winner like Leona Lewis (middle of collage) — but would this winner sell as many records? Answer: No, because it's the backstage access, the community that grows around the competitors, and the story of the winner's rise that engages the audience interest. They make an emotional connection with the artist and the song, and buy it to help it succeed.

Yet, as Scott went on to say, record labels do all the research and auditioning that X Factor and its equivalents do, but they hide it from view, and just say, "here's the winner we've identified: please buy their records". The solution to this is not to create some contrived competition for every new signing (personally I hate it when art gets turned into sport), but it may be to find other ways to involve the audience and give them "backstage" glimpses of artists that help people engage with them. Even a simple blog is a start.

Which are the best social networks for promoting music?

Last week I spoke to students at Westminster University's MA in Music Business Management (I'm always happy to speak to students about digital discovery and social media, so let me know if you run a course that you'd like me to take part in). Lots of good questions in the discussion session, including one at the end that I didn't have time to answer, which was along the lines of "How can we identify which are the best social networks for promoting music?" Here's the bulk of the answer I sent as a follow-up — please let me know if you can improve on it (or challenge it), since I know my knowledge of web metrics is less than comprehensive.

The way I see it you can answer this question with crude figures, more fine-grained measures or with hand-waving arguments. Let's take each in turn.

1 The Crude

Crudely, there are measures from the companies that specialise in website rankings that tell you how which of the social networks get the most traffic. Nielsen and Alexa (here are some old data reported here, but you can get up to the minute data yourself direct from alexa.com)

What makes a good recommender system, and how do you validate it?

If you talk to the people who design systems to produce recommendations tailored to user preferences, you'll see lots of impressively daunting mathematics, with formulae for measuring things like "Mean Absolute Error".

If you talk to the people who sell these systems, you'll hear stories of uncannily perceptive and prescient suggestions that anticipated interests that Jo Consumer didn't even know she had yet.

The information that neither tend to volunteer until asked is that there are no agreed criteria for a perfect, or even a good, recommendation.

Hence you will see reviews of different recommender systems where people put the same starting point into different systems and then compare the results to judge which is the best. But the judgements, like this one, are based on the subjective views of experts: they have no way of articulating what's behind those judgements in a way that could be replicated in other studies. And how reliable are those judgements? Well, when Paul Lamere asked the readers of his blog to compare two sets of recommendations and identify which was done by an expert reviewer and which by machine, over three quarters of the respondents got it wrong. That means if you'd tossed a coin you could have done better than asking these people. (I was one of the few who got it right, but I had already said that there was no way to determine which was which with any confidence, therefore my answer was a guess, and it turned out to be a lucky one.)

So what is a good recommendation, and can we measure value or 'accuracy' at all?

Further reading: notes and bibliography

At university, my supervisor used to say that he would always read the references at the end of a research paper first. His reasoning was that this gave you an easily skimmable indication of both the coverage of the paper and its influences. I'm not sure that many people share this attitude, but if you do, you'll be interested to see that I've put both of the Notes and the Bibliography of Net, Blogs and Rock'n'Roll online.

It's probably more likely that you'll find these useful if you're one of the growing masses who have read the book, and would like easy connections to the articles and books that I mentioned in it. The notes are all hyperlinked, so you don't have to copy out URLs from the back of the book, just click the link to go there directly. Ditto with the bibliography, which has links to further details of each book on LibraryThing and Amazon.com.

Money making scheme?

In a review of Net, Blogs and Rock'n'Roll (the book) in this week's Spectator, Christopher Howse writes

Jennings, a full-time creature of the net, does not deny the popular delusions and madness of crowds that give some music releases undeserved success, but he comes up on page 64 with a practicable scheme for aggregating trusted criticism that should make him money, if someone takes him up on it.

What's the scheme? Well I could be a tease and say that you have to buy the book to find out. But that's not really my style. If I let you in on the secret, perhaps you can tell me something useful? Here's what I assume must be the paragraph in question:

What I’d like to do is compile my own chart. I’d like to be able to select a handful of my friends with whom I share different tastes plus a few other acquaintances, critics or DJs whom I count as tastemakers, and then see a chart aggregating what they have been listening to in the last week. Such a chart would give me useful, trusted pointers to the kind of music I might find interesting. It would also let me know when my friends and I have been listening to the same thing, which could spark further discussions. It is surely technically possible, and I hope someone will build it before long.

Now my question to you is: how do I make money off this?

iPods and identity: a film and a book

What's on your mind when you use your iPod? Are you sealing yourself off from an inhospitable environment, or are you creating a soundtrack to add an imaginary gloss to that environment? These are the sorts of questions being posed by a new film project, Between Ears, by two Dutch filmmakers.

The film is in production and you can contribute to the research via a questionnaire. From the press release,

Between Ears will be a cinematic trip around the world, that leads the viewer along to a surprising diversity of people who shut off from reality around them. With the help of an MP3 player they try to move into a place between their ears. The focus of the film is mainly on the intense experiencing of image and sound, in a way which enables the viewer to identify with the people in the film.

Much of the field of sociological study of people listening to personal music has been staked out by Michael Bull at University of Sussex, going back before the iPod to the days of the original Sony Walkman. The Between Ears site has a video interview with Michael, and his new book, Sound Moves: iPod Culture and Urban Experience, was published just a few weeks ago. Here's my review of his last book, Sounding Out the City (2000).

Audio whiplash from personalised ads with free music downloads

In the last month or so I've been dipping my toes into the ad-supported music download service We7. Their catalogue has expanded significantly since they launched, and there's now enough there that I like for me to have got 40-50 tracks that look interesting without having to spend too much time digging around on the site (the catalogue needs to expand further or the navigation and recommendations need to improve, however, for this to remain the case).

We7's model is based on their MediaGraft technology, which 'grafts' personalised ads onto the front of the track. Generally these are short enough — at 10-20 seconds — not to be too intrusive. Neither were the registration questions bothersome in terms of pumping me for details of my lifestyle. But the consequence of this is that you can end up with song intros like this clip:

Two points about this. One: the personalisation isn't working too well. I have never paid money for games hardware or software. I asked for Sim City for Christmas in 1992, but in the month or two immediately afterwards, I got so annoyed with myself for wasting whole weekends on it (sometimes not even eating properly) that I swore off it and have never gone back.

And, two: even if you leave that aside, listen to that audio whiplash between the ad and the track itself. When you're listening to French chansons from the '20s and '30s, just the tone of the ad is enough to make it come across like an aural invasion, and thus counterproductive in getting across the message.

Given that my selection of tracks is probably a better guide to my personality, lifestyle and buying habits than my age and gender, why not tailor the ads to the music rather than the listener demographics? No need for the clever software (patent pending) then, but the 'creatives' at the ad agencies might have to work a bit harder to think about the context in which their work will be heard.

Detecting exceptions and fraud on recommender systems

I stumbled across this iLike profile when I noticed someone who'd listened to David Bowie over 2 billion times. "Patrick S" has, according to his profile, been alive less than a billion seconds (and iTunes has been available for only the later part of his life), yet he's managed to register over 2 billion plays for 32 separate artists. The data concerned seems to be imported by iLike from iTunes.

Who knows whether this is just idle mischief, a software glitch, or a concerted effort at gaming or 'shilling' the iLike recommender system? Whatever the cause, recommender systems clearly have to detect such incongruent profiles — which can't be hard in cases as gross as this — and make sure the data is excluded from their recommendation algorithms. Cleaning up the profiles for this user and the 32 artists concerned would be a good idea, too, lest they undermine others' confidence in what they see on the site.

Swarmteams: capturing the buzz of trusted fans

If you've read my book, seen my presentation slides, or have just been following this blog reasonably closely, you will know that I'm a sucker for biological metaphors for discovery. I use these metaphors particularly in connection with information foraging and with the idea of people 'pollinating' social networks by spreading the word about their favourites on their profiles and blogs with widgets and playlists.

So I was especially excited to be introduced to Ken Thompson of Belfast-based Swarmteams (thanks to Steve Moore), because he takes biological metaphors even more seriously, and more rigorously, than I do. Take a look at The Bumble Bee, Ken's essays and blog at www.bioteams.com for proof: it's packed with resources and insight into teams and groups in nature (a research theme that goes back at least as far as Peter Kropotkin's Mutual Aid in 1902).

The extra good news is that Swarmteams have just launched an application of their mobile- and online-messaging technology to help bands tap the energies of their most committed fans. The idea, which also closely resembles some of the themes that Howard Rheingold developed in Smart Mobs, is that the top 'alpha' fans of a band will mobilise other fans and friends by spreading the word about new gigs/promotions/offers via SMS, email or instant messaging. The Swarmteams technology has feedback to establish trust in these fan networks, so indiscriminate spammers will quickly be filtered out. But trusted and proactive fans will get rewards for their advocacy.

This application of Swarmteams is a real world realisation and test of some of the armchair future speculation I posted a few weeks ago, so it's really interesting to see how it will turn out. The alpha fans that Swarmteams aims to appeal to are also almost identical to the Savants and Enthusiasts described in other research I've drawn on. This is definitely a space worth watching.